Body mass index is associated with pulmonary gas and blood distribution mismatch in COVID-19 acute respiratory failure. A physiological study

Background The effects of obesity on pulmonary gas and blood distribution in patients with acute respiratory failure remain unknown. Dual-energy computed tomography (DECT) is a X-ray-based method used to study regional distribution of gas and blood within the lung. We hypothesized that 1) regional gas/blood mismatch can be quantified by DECT; 2) obesity influences the global and regional distribution of pulmonary gas and blood; 3) regardless of ventilation modality (invasive vs. non-invasive ventilation), patients’ body mass index (BMI) has an impact on pulmonary gas/blood mismatch. Methods This single-centre prospective observational study enrolled 118 hypoxic COVID-19 patients (92 male) in need of respiratory support and intensive care who underwent DECT. The cohort was divided into three groups according to BMI: 1. BMI<25 kg/m2 (non-obese), 2. BMI = 25–40 kg/m2 (overweight to obese), and 3. BMI>40 kg/m2 (morbidly obese). Gravitational analysis of Hounsfield unit distribution of gas and blood was derived from DECT and used to calculate regional gas/blood mismatch. A sensitivity analysis was performed to investigate the influence of the chosen ventilatory modality and BMI on gas/blood mismatch and adjust for other possible confounders (i.e., age and sex). Results 1) Regional pulmonary distribution of gas and blood and their mismatch were quantified using DECT imaging. 2) The BMI>40 kg/m2 group had less hyperinflation in the non-dependent regions and more lung collapse in the dependent regions compared to the other BMI groups. In morbidly obese patients, gas and blood were more evenly distributed; therefore, the mismatch was lower than in other patients (30% vs. 36%, p < 0.05). 3) An increase in BMI of 5 kg/m2 was associated with a decrease in mismatch of 3.3% (CI: 3.67% to −2.93%, p < 0.05). Neither the ventilatory modality nor age and sex affected the gas/blood mismatch (p > 0.05). Conclusion 1) In a hypoxic COVID-19 population needing intensive care, pulmonary gas/blood mismatch can be quantified at a global and regional level using DECT. 2) Obesity influences the global and regional distribution of gas and blood within the lung, and BMI>40 kg/m2 improves pulmonary gas/blood mismatch. 3) This is true regardless of the ventilatory mode and other possible confounders, i.e., age and sex. Trial Registration Clinicaltrials.gov, identifier NCT04316884, NCT04474249.

4) admission to an ICU at Uppsala University Hospital (Sweden); and 5) at least one chest DECT performed on clinical indication during the ICU stay.For the present analysis, at least one chest dual-energy computed tomography (DECT) performed during ICU stay was required for patient enrollment.Body mass index (BMI) was considered both as a continuous and as a categorical variable and divided into three subgroups defined based on patient BMI at ICU admission: 1) BMI<25 kg/m 2 (non-obese); 2) BMI 25-40 kg/m 2 (overweight and obese); or 3) BMI>40 kg/m 2 (morbidly obese) (Figure 1).STROBE guidelines were followed for data reporting.Power calculation was not conducted before the study, and the sample size was based on the available data.

Clinical data
Comprehensive clinical data were collected on ICU admission, at ICU discharge and on the day of DECT scanning.

DECT scans
DECT imaging was performed on a Siemens Definition Flash dual-source scanner (Siemens Healthineers, Erlangen, Germany).The DECT scans were performed on patients in a supine position with non-ionic iodinated contrast iohexol (Omnipaque 350 mg/ml, GE Healthcare, USA) injected in a peripheral vein at a rate of 4 ml/s with a power injector (Stellant D, Medrad Inc, Indianola, PA, USA) with a total volume of 60-80 ml, according to patient size, with a 50 ml saline flush.To estimate optimal scan delay, test-bolus was performed with a small amount of contrast media (10 ml, injected at 4 ml/s).A region of interest in the pulmonary trunk generated a time-enhancement curve using DynEVA (scanner software).Scanstart was defined as peak +7 s.The scans were acquired in static breath hold, covering the whole lung parenchyma.To avoid streak artifacts due to highly concentrated contrast media in the superior vena cava territory, scans were acquired in the caudocranial direction.The dual-energy protocol setup was 80/Sn 140 kV automatic tube current modulation (Care Dose 4D) with a quality reference of 190 mAs on tube A (automatically given 81 mAs on tube B), collimation 64×0.6 mm, rotation time 0.33 s, and pitch 0.55.Increased tube voltage 100/140Sn was needed for larger patients (above ~90 kg), automatically given a reference of 96/81 mAs.The mean CTDIvol was 15.7±7.0 mGy (Figure 2).The quality of each collected DECT scan was confirmed by one of the authors (TH), a radiologist specialized in thoracic radiology.As the beam rotates around a subject, it exposes a block of tissue from multiple directions.Each voxel is given a density value by using a mathematical process called Fourier analysis at its given position, expressed as a CT number.The CT number is determined by the tissue's attenuation coefficient (µ) which, in turn, depends on the density and atomic number of the material and the energies of the X-ray photon used.Changing the X-ray photon energy will result in a different attenuation coefficient for the same tissue, which is the concept that underlies the DECT technique.All the collected DECT scans were acquired based on clinical indications.

DECT data analysis
The images obtained were stored and processed with DECT post-processing software (Syngo.via,version VB50, Siemens Healthcare), and lung analysis was applied to visualize and quantify the iodine uptake in the lung parenchyma.Two reconstructions from each DECT scan were obtained: 1) virtual non-contrast (VNC) images and 2) virtual contrast (VC) images, mix 0.8 (reconstruction consisting of 80% from tube A with low kV to highlight the iodine contrast), corresponding to the gas and blood distribution maps, respectively.The images were reconstructed into two-dimensional square matrices of 512 × 512 and three-dimensional voxels with dimensions of 0.7461 mm × 0.7461 mm × 1 mm.For each DECT examination, 19 pairs of images, each pair counting gas and blood distribution, were selected along the cranial-caudal axis and uniformly spaced between the apex of the lung and the diaphragmatic dome (Figure 2 and eFigure 1).All the selected images underwent a semiautomatic delineation of the regions of interest corresponding to the lung parenchyma (Figure 2).The resulting gas and blood distribution maps were subsequently divided into ten gravitational levels, as illustrated in Based on previous literature, the HU distribution of the gas distribution maps was classified into four lung compartments (Gattinoni et al., 2001), namely hyperinflated (−1000 to −800 HU), normoinflated (−800 to −500 HU), poorly inflated (−500 to −100 HU), and noninflated (−100 to +100 HU).The HU distribution for the blood distribution maps was instead classified into two compartments: not-perfused (≤0 HU) and perfused (>0 HU) lung.(Uhrig et al., 2015;Ball et al., 2021;Perchiazzi et al., 2022) The HU distribution was analysed based on a histogram (bin breadth of 5 HU) and expressed as an absolute value (number of voxels) as well as a percentage of the total amount of voxels.For each HU distribution profile, the following parameters were collected: 1) the HU peak of the hyperinflated lung; 2) the HU peak of the noninflated lung; 3) the HU peak of perfusion; and 4) the area under the curve for the perfused compartment.For both the pulmonary gas and blood distribution images, the global and regional HU distributions, as well as their mean values for ten gravitational levels, were calculated (Figure 4-5, and eFigure 2).The mean HU value for each of the ten gravitational levels of the lung parenchyma allowed a regional analysis of HU distribution (eFigures 4-5).
Each regional HU mean was then expressed as a percentage of the whole lung HU (Figure 6, and eFigures 5).Based on this, and on the assumption that 1% of regional HU for gas distribution maps correspond to 1% of regional HU for blood distribution maps, an estimation of regional gas/blood mismatch was possible.A linear regression model was applied to BMI and gas/blood mismatch (mismatch = a * BMI + b; where a was the estimated slope, and b the estimated intercept on the y-axis), and used to describe the relation between BMI and gas/blood mismatch as continuous variables.
The whole analysis was conducted using MatLab (Image Processing, Statistics and Machine Learning Toolbox, Release 2022b, The MathWorks, Natick, USA).distribution divided into ten gravitational levels for gas/blood mismatch , where gas exceeding blood in blue and blood exceeding gas content in pink.* To mark differences for the mismatched areas (p>0.05).ANOVA followed by multiple comparison with Bonferroni correction (α<0.05).Abbreviations: ND: non-dependent; D: dependent; HU: Hounsfield unit.

Figure 2 .
Figure 2.Each image voxel attained a representative CT number in Hounsfield units (HU),